The present invention pertains, in general, to process controllers and process monitoring systems that make use of sensors for measuring process variables. In particular it relates to a system that detects and identifies one or more sensor faults, classifies the types of sensor fault, and replaces erroneous sensor values with estimates of the correct process variable values.
Sensor validation is an important step for many model based applications in the process industries. Typical model based applications include model predictive control applications (MPC), and inferential sensing applications in which costly or infrequent measurements, available from laboratory samples or hardware analyzers, are replaced by regularly available inferred values from the model.
In a typical MPC application, steady state optimization is performed to find the optimal target values for the controlled and manipulated variables. If the sensors are faulty, the optimized target values are not valid. Therefore, an effective sensor validation approach that detects and identifies faulty sensors on-line is required. Once a faulty sensor is identified, it is desirable to estimate the fault magnitude and replace it with the best reconstruction in order to maintain the control system on-line even though a sensor has failed.
A typical inferential sensor application is in the area of predictive emissions monitoring systems (PEMS). Federal and/or state regulations may require air-polluting plants to monitor their emissions such as nitrogen oxides (NOx), oxygen (O2), and carbon monoxide (CO). Hardware continuous emissions monitoring systems (CEMS) have both a high initial cost and a high maintenance cost. CEMS can be replaced by PEMS provided the PEMS is shown to be sufficiently accurate and reliable. One of the quality assurance requirements for PEMS is that each sensor that is used in the PEMS model be monitored for failure, and have a strategy for dealing with sensor failure so as to minimize down-time.
The term sensor validation refers, for this patent application, to multivariate model based sensor validation. This is an approach which makes use of redundancy in the plant measurements. Typically sensor measurements exhibit a correlation structure which can be established by a training procedure using collected or historian data. This correlation structure can be monitored online; when the correlation structure is broken a possible sensor fault has occured. However the breaking of this correlation structure could also be due to process upset, process transition, or some other reason unrelated to sensor fault. The main objective is to determine if this really is a sensor fault and, if so, to identify the offending sensor. The various phases of sensor validation can be summarized as:
Detection This phase detects a change in the correlation structure; it may or may not be a sensor fault.
Identification This phase determines if this is a sensor fault and identifies the particular sensor
Estimation This phase estimates the size of the fault which allows reconstruction of the true value and replacement of the faulty value
Classification This phase classifies the type of sensor faultxe2x80x94complete failure, bias, drift, or precision loss
Depending on the particular approach, these phases may overlap. There have been several patents granted that address the topic of multivariate model based sensor validation. The key ones are:
Qin et al. U.S. Pat. No. 5,680,409 xe2x80x9cMethod and Apparatus for detecting and identifying faulty sensors in a processxe2x80x9d
Keeler et al. U.S. Pat. No. 5,548,528 xe2x80x9cVirtual Continuous Emission Monitoring Systemxe2x80x9d
Hopkins et al. U.S. Pat. No. 5,442,562 xe2x80x9cMethod of controlling a manufacturing process using multivariate analysisxe2x80x9d
Qin et al. address sensor validation within the context of process control. The preferred embodiment is based on PCA (Principal Components Analysis) and performs identification through an optimal reconstruction procedure: each sensor value is reconstructed on the assumption it is at fault, then identification and classification is done by tracking indices derived from the reconstruction error.
Keeler et al. address sensor validation explicitly within the context of PEMS. The disclosed system focuses on the inferential sensor technology and the use of neural networks for PEMS. The sensor validation technology uses a sub-optimal reconstruction procedure for identification, does not address classification, and makes use of an xe2x80x9cencoderxe2x80x9d neural network which is a non-linear version of PCA. Encoder networks are also described in Mark Kramer xe2x80x9cNonlinear principal component analysis using autoassociative neural networksxe2x80x9d, AIChE Journal, 37 (2), pp. 233-243 (1991).
Hopkins et al. address sensor validation within the context of process monitoring (multivariate statistical process control), and make use of PCA or PLS (Partial Least Squares). Identification is by means of contribution analysis. Detection is achieved by monitoring principal component xe2x80x9cscoresxe2x80x9d or score statistics and comparing with standard confidence intervals. Identification is by examining the contributions of each original measurement to the offending score. The method does not attempt to classify fault types.
The present invention provides a new apparatus and method for the detection, identification, estimation, reconstruction, and classification of faulty sensors. The approach makes use of steady-state or dynamic process models that can be built from first principles, MPC model identification techniques or from data using statistical methods such as partial least squares (PLS) or principal component analysis. Appendix I describes how to incorporate dynamic MPC models into this invention. One major advantage of this invention is its flexibility to use any of the aforementioned modeling techniques to develop the sensor validation model. In the preferred embodiment, the process model is based on a PCA model in which the number of principal components is chosen to optimize the reconstruction of faulty sensor values as described in Qin and Dunia xe2x80x9cDetermining the number of principal components for best reconstructionxe2x80x9d, Proc. of the 5-th IFAC Symposium on Dynamics and Control of Process Systems, 359-364, Corfu, Greece, Jun. 8-10, 1998.
The detection phase uses a detection index based on the model equation error. An exponentially weighted moving average (EWMA) filter is applied to the detection index to reduce false alarms due to temporary transients. The filtered detection index (FDI) is compared to a statistically derived threshold in order to detect possible faults. Detection of a possible fault condition triggers the identification phase of the invention.
The key component of this invention is the identification phase. To determine whether a detection alarm is due to one or more faulty sensors, and to identify the offending sensor(s), a series of detectors are constructed which are insensitive to one subset of faults but most sensitive to the others. These detectors are based on structured residuals (SRs) constructed by means of a novel approach referred to a structured residual approach with maximized sensitivity (SRAMS). Structured residuals are generally described in Gertler and Singer, xe2x80x9cA new structural framework for parity equation based failure detection and isolationxe2x80x9d, Automatica 26:381-388, 1990. An exponentially weighted moving average (EWMA) filter is applied to the SRs to reduce false alarms due to temporary transients. The SRs are also squared and normalized so as to equitably compare different SRs. Identification is achieved by comparing these normalized squared filtered structured residuals (NSFSRs) to statistically inferred confidence limits. In addition to NSFSRs, indices based on the accumulation of the normalized structured residuals (NSRs) from the time of detection are monitored and compared for use in the identification of faulty sensors. Two such indices are the generalized likelihood ratio (GLR) index, and the normalized cumulative variance (NCUMVAR) index. The NCUMVAR index is primarily useful for identifying sensors with precision degradation.
The fault magnitude is then optimally estimated based on the model, faulty data, and the assumption that the faulty sensors have been correctly identified. This uses public domain prior art described, for example, Martens and Naes xe2x80x9cMultivariate Calibrationxe2x80x9d, John Wiley and Sons, New York, 1989. Knowledge of the fault direction (known from the identification of the faulty sensors) and the estimated fault magnitude is then used to reconstruct estimates of the correct sensor values.
The fault classification phase provides diagnostic information as to the type of sensor fault. Specifically, four types of fault are considered: Complete Failure, Bias, Drift, and Precision Loss. Complete failure is determined by performing a regression analysis on an identified faulty sensor""s measured values, and is indicated by the statistical inference that the regression line has zero slope. The other three types of fault are classified by performing a regression analysis on the estimated fault sizes since the time of identification. Bias is indicated by the statistical inference that the estimated fault size regression line has zero slope and non-zero offset, and has small residual error. Drift is indicated by the statistical inference that the estimated fault size regression line has non-zero slope, and has small residual error. Precision Loss is indicated by the statistical inference that estimated fault size regression line has zero slope, zero offset, and significant residual error. Precision Loss is also indicated if the fault is identifiable only by the NCUMVAR index.